International Journal of Technology and Applied Science

E-ISSN: 2230-9004   •   Impact Factor: 9.914

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Volume 17 Issue 4
April 2026

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Soil Analysis and Crop Recommendation using Machine Learning

Author(s) S. Gopika, R. Deekshitha, D. Shekshavali
Country India
Abstract India’s agriculture sector plays a key role in the growth of the country’s economy. Crop production mainly depends on soil conditions, and selecting the right crop for a given soil is very important for achieving better yield. Many young farmers face difficulty in choosing suitable crops because soil properties vary across regions. Wrong crop selection leads to reduced productivity and financial loss. To address this issue, this project proposes a machine learning–based crop recommendation system using soil data. The system uses a soil dataset containing parameters such as pH value, moisture level, temperature, and nutrient content. These features are analyzed using machine learning algorithms such as Decision Tree, Random Forest, and Support Vector Machine to learn patterns between soil conditions and crop suitability. Based on the given soil data, the trained model predicts the most suitable crop for cultivation. Experimental results show that the proposed system achieves high prediction performance, with an accuracy of approximately 97%. The system is simple, cost-effective, and useful for guiding farmers, especially beginners, in selecting suitable crops and improving agricultural productivity.
Keywords Soil Data, Crop Recommendation, Machine Learning, Smart Agriculture, decision tree, Random forest algorithm, SVM.
Field Engineering
Published In Volume 17, Issue 4, April 2026
Published On 2026-04-05
Cite This Soil Analysis and Crop Recommendation using Machine Learning - S. Gopika, R. Deekshitha, D. Shekshavali - IJTAS Volume 17, Issue 4, April 2026.
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